Degraded gas turbine tuning and control systems, computer program products and related methods

ABSTRACT

Various embodiments include a system having: at least one computing device configured to tune a set of gas turbines (GTs) by performing actions including: commanding each GT in the set of GTs to a base load level, based upon a measured ambient condition for each GT; commanding each GT in the set of GTs to adjust a respective output to match a nominal mega-watt power output value, and subsequently measuring an actual emissions value for each GT; adjusting an operating condition of each GT in the set of GTs based upon a difference between the respective measured actual emissions value and a nominal emissions value at the ambient condition; and calculating a degradation for each GT in the set of GTs over a period.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application relates to co-pending U.S. patent application Ser. No.______ (Attorney Dkt. No. 276712-1), U.S. patent application Ser. No.______ (Attorney Dkt. No. 276701-1), U.S. patent application Ser. No.______ (Attorney Dkt. No. 276802-1), U.S. patent application Ser. No.______ (Attorney Dkt. No. 276809-1), and U.S. patent application Ser.No. ______ (Attorney Dkt. No. 277317-1) all filed concurrently herewithon ______.

FIELD OF THE INVENTION

The subject matter disclosed herein relates to tuning and controlsystems. More particularly, the subject matter disclosed herein relatesto tuning and control systems for degraded gas turbines.

BACKGROUND OF THE INVENTION

At least some known gas turbine engines include controllers that monitorand control their operation. Known controllers govern the combustionsystem of the gas turbine engine and other operational aspects of thegas turbine engine using operating parameters of the engine. At leastsome known controllers receive operating parameters that indicate thegas turbine engine's present operating state, define operationalboundaries by way of physics-based models or transfer functions, andapply the operating parameters to the operational boundary models.Additionally, at least some known controllers also apply the operatingparameters to scheduling algorithms, determine error terms, and controlboundaries by adjusting one or more gas turbine engine controleffectors. However, at least some operating parameters may be unmeasuredparameters, such as parameters that may be impractical to measure usingsensors. Some of such parameters include firing temperature (i.e., stage1 turbine vane exit temperature), combustor exit temperature, and/orturbine stage 1 nozzle inlet temperature.

At least some known gas turbine engine control systems indirectlycontrol or monitor unmeasured operating parameters using measuredparameters, such as compressor inlet pressure and temperature,compressor exit pressure and temperature, turbine exhaust pressure andtemperature, fuel flow and temperature, ambient conditions, and/orgenerator power. However, there is uncertainty in the values of indirectparameters, and the associated gas turbine engines may need tuning toreduce combustion dynamics and emissions. Because of the uncertainty ofunmeasured parameters, design margins are used for gas turbine enginesthat include such known control systems. Using such design margins mayreduce the performance of the gas turbine engine at many operatingconditions in an effort to protect against and accommodate worst-caseoperational boundaries. Moreover, many of such known control systems maynot accurately estimate firing temperature or exhaust temperature of thegas turbine engine, which may result in a less efficient engine andvariation from machine-to-machine in facilities with more than one gasturbine engine.

It has proven difficult to reduce variation in firing temperature frommachine-to-machine for industrial gas turbines. For example, firingtemperature is a function of many different variables, includingvariations in the components of the gas turbine and their assembly.These variations are due to necessary tolerances in manufacturing,installation, and assembly of the gas turbine parts. In addition, thecontrols and sensors used to measure the operating parameters of the gasturbine contain a certain amount of uncertainty in their measurements.It is the uncertainty in the measurement system used to sense the valuesof the measured operating parameters and the machine componentvariations that necessarily result in variation of the unmeasuredoperating parameters of the gas turbine engine, such as the firingtemperature. The combination of these inherent inaccuracies makes itdifficult to achieve the design firing temperature of a gas turbineengine at a known set of ambient conditions and results in firingtemperature variation from machine-to-machine.

BRIEF DESCRIPTION OF THE INVENTION

Various embodiments include a system having: at least one computingdevice configured to tune a set of gas turbines (GTs) by performingactions including: commanding each GT in the set of GTs to a base loadlevel, based upon a measured ambient condition for each GT; commandingeach GT in the set of GTs to adjust a respective output to match anominal mega-watt power output value, and subsequently measuring anactual emissions value for each GT; adjusting an operating condition ofeach GT in the set of GTs based upon a difference between the respectivemeasured actual emissions value and a nominal emissions value at theambient condition; and calculating a degradation for each GT in the setof GTs over a period.

A first aspect includes a system having: at least one computing deviceconfigured to tune a set of gas turbines (GTs) by performing actionsincluding: commanding each GT in the set of GTs to a base load level,based upon a measured ambient condition for each GT; commanding each GTin the set of GTs to adjust a respective output to match a nominalmega-watt power output value, and subsequently measuring an actualemissions value for each GT; adjusting an operating condition of each GTin the set of GTs based upon a difference between the respectivemeasured actual emissions value and a nominal emissions value at theambient condition; and calculating a degradation for each GT in the setof GTs over a period.

A second aspect includes a computer program product having program code,which when executed by at least one computing device, causes the atleast one computing device to tune a set of gas turbines (GTs) byperforming actions including: commanding each GT in the set of GTs to abase load level, based upon a measured ambient condition for each GT;commanding each GT in the set of GTs to adjust a respective output tomatch a nominal mega-watt power output value, and subsequently measuringan actual emissions value for each GT; adjusting an operating conditionof each GT in the set of GTs based upon a difference between therespective measured actual emissions value and a nominal emissions valueat the ambient condition; and calculating a degradation for each GT inthe set of GTs over a period.

A third aspect includes a computer-implemented method of tuning a set ofgas turbines (GTs), performed using at least one computing device, themethod including: commanding each GT in the set of GTs to a base loadlevel, based upon a measured ambient condition for each GT; commandingeach GT in the set of GTs to adjust a respective output to match anominal mega-watt power output value, and subsequently measuring anactual emissions value for each GT; adjusting an operating condition ofeach GT in the set of GTs based upon a difference between the respectivemeasured actual emissions value and a nominal emissions value at theambient condition; and calculating a degradation for each GT in the setof GTs over a period.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings that depict various embodiments of the invention, in which:

FIG. 1 shows a schematic illustration of a gas turbine engine (GT),including a control system, according to various embodiments of theinvention.

FIG. 2 shows a schematic view of a control architecture that may be usedwith the control system of FIG. Ito control operation of the GT,according to various embodiments of the invention.

FIG. 3 shows a graphical depiction of a probabilistic simulation of theoperating states of a statistically significant number of GT engines ofFIG. 1 using a model of the GT used by the control system of FIG. 1.

FIG. 4 shows a flow diagram illustrating a method according to variousembodiments of the invention.

FIG. 5 shows a graphical depiction of a process illustrated in the flowdiagram of FIG. 4, in a two-dimensional Mega-Watt-power v. Emissions(NO_(x)) graph.

FIG. 6 shows a graphical depiction of a process illustrated in the flowdiagram of FIG. 4, in a two-dimensional Mega-Watt-power v. Emissions(NO_(x)) graph.

FIG. 7 shows a graphical depiction of a process illustrated in the flowdiagram of FIG. 4, in a three-dimensional Mega-Watt-power v. Emissions(NO_(x)) v. firing temperature (T4) graph.

FIG. 8 shows a graphical depiction of a degraded set of GT engines in aMega-Watt-power v. Emissions (NO_(x)) graph, generated according to aprocess illustrated in the flow diagram of FIG. 4.

FIG. 9 shows an illustrative environment including a control systemaccording to various embodiments of the invention.

It is noted that the drawings of the invention are not necessarily toscale. The drawings are intended to depict only typical aspects of theinvention, and therefore should not be considered as limiting the scopeof the invention. In the drawings, like numbering represents likeelements between the drawings.

DETAILED DESCRIPTION OF THE INVENTION

As indicated above, subject matter disclosed herein relates to tuningand control systems. More particularly, the subject matter disclosedherein relates to tuning and control systems for degraded gas turbines.

Probabilistic control is a methodology for setting the operating stateof a gas turbine (GT) based upon measured output (in mega-watts, MW) andmono-nitrogen oxides NO and NO₂ (nitric oxide and nitrogen dioxide),collectively referred to as NO_(x) emissions. As described herein,various embodiments provide tuning and control of a GT where errors inNO_(x) measurements exist. Conventional approaches exist to calculateand tune control mechanisms where measurement errors (outputmeasurements in MW) exist, but no conventional approaches are designedto account for and tune GT control functions in view of NO_(x)measurements.

As used herein, term P50 GT or P50 machine refers to a mean (or,nominal) gas turbine or similar machine in a fleet. Parametersassociated with this P50 measure are considered ideal, and are rarely ifever attained in an actual gas turbine. Other terms used herein caninclude: a) firing temperature (T4), which is the average temperaturedownstream of a first-stage nozzle, but upstream of the first rotatingbucket in the turbine (e.g., GT); and b) T3.9, which is the combustiontemperature in the gas turbine, and is higher than the firingtemperature. The firing temperature, as is known in the art, cannot bemeasured, but is inferred from other measurements and known parameters.As used herein, the term, “indicated firing temperature” refers to thefiring temperature as indicated by one or more components of controlequipment, e.g., a control system monitoring and/or controlling GTcomponents. The “indicated” firing temperature represents the bestestimate of the firing temperature from conventional sensing/testingequipment connected with the GT control system.

Additionally, as described herein, the term “base load” for a particulargas turbine can refer to the maximum output of the gas turbine at ratedfiring temperature. Further, as described herein, and known in the art,base load for a given gas turbine will change based upon changes inambient operating conditions. Sometimes base load is referred to as“Full Speed Full Load” in the art. Further, it is understood that NOx issensitive to fuel composition, and as such, it is accounted for in anytuning processes conducted in a gas turbine (including tuning processesdescribed herein).

According to various embodiments, an approach can include the followingprocesses:

1) Commanding one or more gas turbines (e.g., in a fleet) to a designedbase load (MW value, NO_(x) value), based upon a measured ambientcondition. As described herein, in an ideal situation, the GT(s) should,in an ideal scenario, converge to P50 (nominal) operating parameters,including a P50 MW (nominal output) value and P50 NO_(x) (emissions)value. However, as indicated herein, this does not occur in real-worldoperations;

2) Commanding the one or more GTs to adjust its output to match to P50MW (nominal output) value, and measuring the actual NOx value. As notedherein, this process will likely help to bring each GT's actual NOxvalue closer to the P50 NO_(x) value, but does not fully succeed in thatgoal. Additionally, this output adjustment does not address anotherconcern, that being the elevated firing temperature relative to itsdesired level; and

3) Adjusting each GT's operating condition based upon its difference(Delta NO_(x)) between the measured actual NOx value (process 2) and theexpected, P50 NO_(x) value for the ambient condition. The Delta NO_(x)value can be translated to a Delta MW value (representing the differencebetween the GT's actual output and the P50 MW level) for each GT usingconventional approaches. In this process, each GT that deviates from theP50 MW value, has its operating condition adjusted by a fixed fractionof the Delta MW value (as converted from the Delta NO_(x) value) suchthat it approaches and then reaches the Delta MW value for that GT. Thisadjustment will move each GT onto a line in MW/NO_(x) space that isorthogonal to the P50 MW/P50 NO_(x) characteristic for that GT. Theabove-noted general processes are described in further detail herein.

In the following description, reference is made to the accompanyingdrawings that form a part thereof, and in which is shown by way ofillustration specific example embodiments in which the present teachingsmay be practiced. These embodiments are described in sufficient detailto enable those skilled in the art to practice the present teachings andit is to be understood that other embodiments may be utilized and thatchanges may be made without departing from the scope of the presentteachings. The following description is, therefore, merely illustrative.

FIG. 1 shows a schematic illustration of a gas turbine engine (GT) 10including a control system 18, according to various embodiments. Invarious embodiments, gas turbine engine 10 includes a compressor 12, acombustor 14, a turbine 16 drivingly coupled to compressor 12, and acomputer control system, or controller 18. An inlet duct 20 tocompressor 12 channels ambient air and, in some instances, injectedwater to compressor 12. Duct 20 may include ducts, filters, screens, orsound absorbing devices that contribute to a pressure loss of ambientair flowing through inlet duct 20 and into inlet guide vanes (IGV) 21 ofcompressor 12. Combustion gasses from gas turbine engine 10 are directedthrough exhaust duct 22. Exhaust duct 22 may include sound adsorbingmaterials and emission control devices that induce a backpressure to gasturbine engine 10. An amount of inlet pressure losses and backpressuremay vary over time due to the addition of components to inlet duct 20and exhaust duct 22, and/or as a result of dust or dirt clogging inletduct 20 and exhaust duct 22, respectively. In various embodiments, gasturbine engine 10 drives a generator 24 that produces electrical power.

Various embodiments are described which measure, analyze and/or controla set of GTs, which may include one or more gas turbine engines (GTs),e.g., in a fleet. It is understood that these approaches are similarlyapplied to a single GT as two or more GTs. It is further understood thatthe term “set” as used herein can mean 1 or more.

In various embodiments, a plurality of control sensors 26 detect variousoperating conditions of gas turbine engine 10, generator 24, and/or theambient environment during operation of gas turbine engine 10. In manyinstances, multiple redundant control sensors 26 may measure the sameoperating condition. For example, groups of redundant temperaturecontrol sensors 26 may monitor ambient temperature, compressor dischargetemperature, turbine exhaust gas temperature, and/or other operatingtemperatures the gas stream (not shown) through gas turbine engine 10.Similarly, groups of other redundant pressure control sensors 26 maymonitor ambient pressure, static and dynamic pressure levels atcompressor 12, turbine 16 exhaust, and/or other parameters in gasturbine engine 10. Control sensors 26 may include, without limitation,flow sensors, speed sensors, flame detector sensors, valve positionsensors, guide vane angle sensors, and/or any other device that may beused to sense various operating parameters during operation of gasturbine engine 10.

As used herein, the term “parameter” refers to characteristics that canbe used to define the operating conditions of gas turbine engine 10,such as temperatures, pressures, and/or gas flows at defined locationswithin gas turbine engine 10. Some parameters are measured, i.e., aresensed and are directly known, while other parameters are calculated bya model and are thus estimated and indirectly known. Some parameters maybe initially input by a user to controller 18. The measured, estimated,or user input parameters represent a given operating state of gasturbine engine 10.

A fuel control system 28 regulates an amount of fuel flow from a fuelsupply (not shown) to combustor 14, an amount split between primary andsecondary fuel nozzles (not shown), and an amount mixed with secondaryair flowing into combustor 14. Fuel control system 28 may also select atype of fuel for use in combustor 14. Fuel control system 28 may be aseparate unit or may be a component of controller 18.

Controller (control system) 18 may be a computer system that includes atleast one processor (not shown) and at least one memory device (notshown) that executes operations to control the operation of gas turbineengine 10 based at least partially on control sensor 26 inputs and oninstructions from human operators. The controller may include, forexample, a model of gas turbine engine 10. Operations executed bycontroller 18 may include sensing or modeling operating parameters,modeling operational boundaries, applying operational boundary models,or applying scheduling algorithms that control operation of gas turbineengine 10, such as by regulating a fuel flow to combustor 14. Controller18 compares operating parameters of gas turbine engine 10 to operationalboundary models, or scheduling algorithms used by gas turbine engine 10to generate control outputs, such as, without limitation, a firingtemperature. Commands generated by controller 18 may cause a fuelactuator 27 on gas turbine engine 10 to selectively regulate fuel flow,fuel splits, and/or a type of fuel channeled between the fuel supply andcombustors 14. Other commands may be generated to cause actuators 29 toadjust a relative position of IGVs 21, adjust inlet bleed heat, oractivate other control settings on gas turbine engine 10.

Operating parameters generally indicate the operating conditions of gasturbine engine 10, such as temperatures, pressures, and gas flows, atdefined locations in gas turbine engine 10 and at given operatingstates. Some operating parameters are measured, i.e., sensed and aredirectly known, while other operating parameters are estimated by amodel and are indirectly known. Operating parameters that are estimatedor modeled, may also be referred to as estimated operating parameters,and may include for example, without limitation, firing temperatureand/or exhaust temperature. Operational boundary models may be definedby one or more physical boundaries of gas turbine engine 10, and thusmay be representative of optimal conditions of gas turbine engine 10 ateach boundary. Further, operational boundary models may be independentof any other boundaries or operating conditions. Scheduling algorithmsmay be used to determine settings for the turbine control actuators 27,29 to cause gas turbine engine 10 to operate within predeterminedlimits. Typically, scheduling algorithms protect against worst-casescenarios and have built-in assumptions based on certain operatingstates. Boundary control is a process by which a controller, such ascontroller 18, is able to adjust turbine control actuators 27, 29 tocause gas turbine engine 10 to operate at a preferred state.

FIG. 2 shows a schematic view of an example control architecture 200that may be used with controller 18 (shown in FIG. 1) to controloperation of gas turbine engine 10 (shown in FIG. 1). More specifically,in various embodiments, control architecture 200 is implemented incontroller 18 and includes a model-based control (MBC) module 56. MBCmodule 56 is a robust, high fidelity, physics-based model of gas turbineengine 10. MBC module 56 receives measured conditions as input operatingparameters 48. Such parameters 48 may include, without limitation,ambient pressure and temperature, fuel flows and temperature, inletbleed heat, and/or generator power losses. MBC module 56 applies inputoperating parameters 48 to the gas turbine model to determine a nominalfiring temperature 50 (or nominal operating state 428). MBC module 56may be implemented in any platform that enables operation of controlarchitecture 200 and gas turbine engine 10 as described herein.

Further, in various embodiments, control architecture 200 includes anadaptive real-time engine simulation (ARES) module 58 that estimatescertain operating parameters of gas turbine engine 10. For example, inone embodiment, ARES module 58 estimates operational parameters that arenot directly sensed such as those generated by control sensors 26 foruse in control algorithms. ARES module 58 also estimates operationalparameters that are measured such that the estimated and measuredconditions can be compared. The comparison is used to automatically tuneARES module 58 without disrupting operation of gas turbine engine 10.

ARES module 58 receives input operating parameters 48 such as, withoutlimitation, ambient pressure and temperature, compressor inlet guidevane position, fuel flow, inlet bleed heat flow, generator power losses,inlet and exhaust duct pressure losses, and/or compressor inlettemperature. ARES module 58 then generates estimated operatingparameters 60, such as, without limitation, exhaust gas temperature 62,compressor discharge pressure, and/or compressor discharge temperature.In various embodiments, ARES module 58 uses estimated operatingparameters 60 in combination with input operating parameters 48 asinputs to the gas turbine model to generate outputs, such as, forexample, a calculated firing temperature 64.

In various embodiments, controller 18 receives as an input, a calculatedfiring temperature 52. Controller 18 uses a comparator 70 to comparecalculated firing temperature 52 to nominal firing temperature 50 togenerate a correction factor 54. Correction factor 54 is used to adjustnominal firing temperature 50 in MBC module 56 to generate a correctedfiring temperature 66. Controller 18 uses a comparator 74 to compare thecontrol outputs from ARES module 58 and the control outputs from MBCmodule 56 to generate a difference value. This difference value is theninput into a Kalman filter gain matrix (not shown) to generatenormalized correction factors that are supplied to controller 18 for usein continually tuning the control model of ARES module 58 thusfacilitating enhanced control of gas turbine engine 10. In analternative embodiment, controller 18 receives as an input exhausttemperature correction factor 68. Exhaust temperature correction factor68 may be used to adjust exhaust temperature 62 in ARES module 58.

FIG. 3 is a graph that shows a probabilistic simulation of the operatingstates of a statistically significant number of the gas turbine engine10 of FIG. 1 using the model of gas turbine engine used by controller18. The graph represents power output versus firing temperature of gasturbine engine 10. Line 300 is the linear regression model for theplurality of data points 308. Lines 302 represent the 99% predictioninterval corresponding to data points 308. Further, line 304 representsthe nominal or design firing temperature 50 for gas turbine engine 10,and line 306 represents a nominal or design power output for gas turbineengine 10. In various embodiments, the probabilistic simulation shown inFIG. 2 shows an approximate variance in firing temperature of 80 units.This variance may be attributed to the component tolerances of gasturbine engine 10, and the measurement uncertainty of controller 18 andcontrol sensors 26.

Described herein are approaches for tuning gas turbine engine 10 thatfacilitates reducing variation in the actual gas turbine engine 10operating state, e.g., firing temperature and/or exhaust temperature,which facilitates reducing variation in power output, emissions, andlife of gas turbine engine 10. The probabilistic control approachesdescribed herein may be implemented as either a discrete process to tunegas turbine engine 10 during installation and at various periods, or maybe implemented within controller 18 to run periodically at apredetermined interval and/or continuously during operation of gasturbine engine 10. These approaches do not measure gas turbine firingtemperature directly because firing temperature is an estimatedparameter, as previously discussed. These probabilistic controlapproaches, however, can yield directly measured parameters that arestrong indicators of the firing temperature of the gas turbine engine10, and allow for improved control over the firing temperature in a gasturbine engine 10.

FIG. 4 shows a flow diagram illustrating a method performed according tovarious embodiments. As described herein, the method can be performed(e.g., executed) using at least one computing device, implemented as acomputer program product (e.g., a non-transitory computer programproduct), or otherwise include the following processes:

Process P1: commanding each GT 10 in the set of GTs to a base load level(e.g., target indicated firing temperature), based upon a measuredambient condition for each GT 10. As noted herein, the base load (with atarget indicated firing temperature) is associated with a mega-wattpower output value and an emissions value for the measured ambientcondition. As further noted herein, in response to commanding each GT 10in the set of GTs to the base load level, each GT 10 does not attain atleast one of the nominal MW output value (P50 MW) or the nominalemissions value (P50 NO_(x)). According to various embodiments, theprocess of commanding each GT 10 in the set of GTs to adjust arespective output to match the nominal MW output value moves an actualemissions value for each GT 10 closer to the nominal emissions valuewithout matching the nominal emissions value;

Process P2: commanding each GT 10 in the set of GTs to adjust arespective output to match a nominal mega-watt power output value, andsubsequently measuring an actual emissions value for each GT 10. Invarious embodiments, process P2 can further include converting thedifference between the respective measured actual emissions value andthe nominal emissions value for each GT 10 into a difference between arespective mega-watt power output value and the nominal mega-watt poweroutput value at the ambient condition value for each GT 10; and

Process P3: adjusting an operating condition of each GT 10 in the set ofGTs based upon a difference between the respective measured actualemissions value and a nominal emissions value at the ambient condition.According to various embodiments, the process of adjusting the operatingcondition of each GT 10 includes adjusting the operating condition ofeach GT 10 in the set of GTs by a fixed fraction of the differencebetween the respective mega-watt power output value and the nominalmega-watt power output value, such that the output of each GT 10approaches and then reaches a respective nominal mega-watt power outputvalue. According to various embodiments, adjusting of the operatingcondition of each GT 10 in the set of GTs by the fixed fraction of thedifference between the respective mega-watt power output value and thenominal mega-watt power output value aligns each GT 10 on a line ingraphical space plotting mega-watts versus emissions that is orthogonalto a nominal mega-watt power output/nominal emissions characteristic foreach GT 10.

FIGS. 5-7 show graphical depictions, via MW-power v. Emissions (NO_(x))graphs, of the processes described in FIG. 4, with respect to an exampledata set representing a set (plurality) of GTs (similar to GT 10). Alldata points shown in FIGS. 5-6 represent MW-power v. Emissions (NO_(x))at indicated firing temperatures, where “indicated” firing temperatureis the firing temperature as displayed or otherwise outputted by thecontroller of GT 10. That is, the “indicated” firing temperature is notnecessarily the actual firing temperature (which, as described herein,cannot be accurately measured), but instead, the firing temperature asestimated by the controller (and related equipment) of the GT 10.

As shown in this example, e.g., in FIG. 5, the center point of line GLis a function of the mean firing temperature (T4) of the set of GTs. Themean combustion temperature (T3.9) is a function of the mean firingtemperature, and is greater than the mean firing temperature. Notedherein, as the mean firing temperature increases, so will the meancombustion temperature, meaning that line GL will shift to a greaterMW/NO_(x) value, while remaining orthogonal to line RL, which definesthe MW/NO_(x) characteristic for the mean GT in the set at base load.The two lines labeled BL bound line GL, and define the statisticalvariation among the set of GTs, to two sigma (Σ), from the mean line RL.The inventors have discovered through empirical testing that lines BLrepresent a +/−10 degree span in actual firing temperature (T4) fromline RL, as measured along a given line orthogonal to line RL. FIG. 6shows the graphical depiction of FIG. 5, with the addition of indicatorsfor the Mean T4 (firing temperature) at distinct example MW/NO_(x)values for a fleet of GTs, along lines orthogonal to RL (MW/NO_(x)characteristic) and lines BL. Mean T4 (B) and Mean T4 (P) in thisexample, illustrate example fleets at T4 =2,410 degrees F. and T4 =2,430degrees F., respectively. FIG. 6 also illustrates a line PL, which is anexample of a single GT along a firing temperature (T4) “sweep” orvariation orthogonal with the MW/NOx characteristic line. PL shows howthe MW/NOx varies by a changing firing temperature (T4).

FIG. 7 shows a three-dimensional graphical depiction of the process P3(FIG. 4), namely, adjusting an operating condition of each GT in the setof GTs based upon a difference between the respective measured actualemissions value and a nominal emissions value at the ambient condition.That is, as shown in FIG. 7, the GL plane, defined by the plane of theGL (FIGS. 5-6) across firing temperature (T4) space, illustrates a modelof where the set of GTs operate in the firing temperature (T4) space.That is, although actual firing temperature (T4) cannot be directlymeasured for each GT in the set of GTs, the GL plane represents the mostaccurate model of the firing temperature of GTs within the set of GTs.According to the various embodiments, process P3 includes adjusting anoperating condition of each GT based upon a difference between itsrespective measured actual emissions value (NO_(x) value) and a nominal(average) emissions value (NO_(x) value) for the respective GT. That is,according to various embodiments, an operating condition of each GT isadjusted such that its MW/NO_(x) value intersects GL in two-dimensionalspace (FIGS. 5-6), and the GL plane in three-dimensional space (FIG. 7).The intersection of the nominal (P50) MW/NOx lines and the GL planerepresents the most accurate model of the desired mean actual firingtemperature (P4), and by tuning each GT 10 to approach that GL plane,firing temperature variation is reduced across the fleet, increasing thelife of the fleet.

The GL (and the GL plane) is a characteristic of how gas turbines aredesigned and built, and in MW/NO_(x) space, its center is at theintersection of P50 MW and P50 NO_(x) for the particular type of GT 10in a fleet. The length of GL in two-dimensional space (e.g., the spacebetween BLs, FIGS. 5-6) is defined by the GT-to-GT hardware variationfor a given type of GT (e.g., physical variances in the manufacture oftwo machines to the same specifications). By altering operatingconditions of a GT 10 in order to align the MW/NO_(x) value for that GT10 with the GL (and GL plane), the variation in the actual firingtemperature (T4) is minimized.

It is understood that in a non-degraded set (e.g., fleet), that is, a“new” or otherwise “clean” fleet of GTs, the above-noted processes cantune (reduce) the variation in firing temperature across the GTs in thatfleet. That is, the processes described and depicted in conjunction withFIGS. 4-7 can reduce the range of variation in three-dimensionalMW-NOx-T4 space across the fleet. The range can effectively be referredto as a three-dimensional box, which has a center and a perimeter.According to various embodiments herein, as a set (e.g., fleet) of GTsdegrades (that is, subject to wear-and-tear), this three-dimensional box(in MW-NOx-T4 space) will move within the three-dimensional space, andis likely to expand. Further, because each individual GT is a member ofa given set (fleet), the various embodiments can designate anyparticular GT as a center point of the fleet box (MW-NOx-T4), with astatistical uncertainty assigned to the size (variance) of the box.

Being able to predict where a set of GTs will operate within MW-NOx-T4space may provide several advantages, e.g., a) the impact on performanceand lifespan may become more predictable, with less uncertainty; b) ifthe operation of a the fleet (and any individual machine) is changed,to, e.g., maintain a “new” MW power output, then the consequences forits lifespan may be more predictable; and c) novel operating schemesbecome feasible, such as a flat-line MW rating across a GT interval,with a de-rate at the “new” condition, and an over-fire during thelatter part of the interval such that MW are constant and lifespan isapproximately maintained.

As described herein, a set of parameters (a “box”) in MW-NOx-T4 spacecan be created by: a) commanding each GT in the set of GTs to a baseload level, based upon a measured ambient condition for each GT;commanding each GT in the set of GTs to adjust a respective output tomatch a nominal mega-watt power output value, and subsequently measuringan actual emissions value for each GT; and adjusting an operatingcondition of each GT in the set of GTs based upon a difference betweenthe respective measured actual emissions value and a nominal emissionsvalue at the ambient condition. This set of parameters minimizesvariation in GT operating parameters (e.g., MW output, NOx, and fuelflow) and true (not measurable) firing temperature. In service, theperformance of a GT degrades due to long-term degradation (normalwear-and-tear), for reasons such as compressor and turbine clearancechanges, changes in chargeable and non-chargeable flows, etc. Generally,long-term degradation is difficult to restore without replacing major GTcomponents. The performance of a GT may also degrade due to what isreferred to as “fouling” of the compressor, caused by, e.g., build-up ofdeposits of material from the compressor inlet air. This “fouling” cansometimes be remediated by washing the compressor, e.g., with water.

As described herein, various approaches include determining how thedetermined MW-NOx-T4 parameters for a fleet of GTs move over time. Thatis, a fourth variable (T) is introduced into the approaches describedherein. Because degradation is a non-linear quantity (in terms of time),it can be considered in two parts for the purposes of modeling: a) anincreasingly non-linear bias, over time; and b) a random variation withan increasing variance (standard deviation), over time. According tovarious embodiments, the degradation is evaluated for a fleet atspecific times, e.g., 6,000 operating hours, 12,000 operating hours,24,000 operating hours, etc. Evaluating degradation at specific timesallows bias (part a) to be treated as a mean shift, along with assigninga random variable (part b) to a plurality (e.g., 5-10) hardwarevariables.

Returning to the flow diagram of FIG. 4, various additional processesare illustrated. According to various embodiments, process P4, followingprocess P3, can include

Process P4 can include a plurality of sub-processes, including ProcessP4A: selecting a set of degradation variables, with assigned randomlyselected degradation values, for evaluating the degradation of the set(fleet) of GTs. After selecting the set of degradation variables, andfollowing process P4A, process P4B can include commanding each GT in theset of GTs to adjust to base load conditions, based upon the adjustmentmade in process P3. Process P4C (following process P4B) can includecalculating the MW-NOx-T4 parameters for each GT in the set of GT,following the adjustment to base load conditions for each GT. PathP3-P4A-P4B-P4C is denoted as a “Process 3 study.”

In various embodiments, the flow may bypass Process P3, and this PathP2-P4A-P4B-P4C is denoted as a “Process 2 study.” In various additionalembodiments the flow may bypass Processes P2 and P3, and this PathP1-P4A-P4B-P4C is denoted as a “Process 1 study.” Each of the Process 3study, the Process 2 study and the Process 1 study can provide MW-NOx-T4parameters for the fleet of GTs (and each individual GT) caused bydegradation.

FIG. 8 shows a graphical depiction, via a MW-power v. Emissions (NO_(x))graph, of degradation of the set of GTs 10, as described in processesP4-P6 with reference to FIG. 4. As shown, (green line) GL is bothshifted downward (orthogonally with RL) relative to FIGS. 5-6, but it isalso extended in the MW-NOx space relative to its position in FIGS. 5-6(shown as GL′). That is, as the set of degraded GTs operates at a lowerMW-NOx-T4 level than the non-degraded set, but additionally, theMW-NOx-T4 space encompassing the set of GTs is expanded relative to thenon-degraded set.

Returning to the flow chart in FIG. 4, process P4 (selecting the set ofdegradation variables) can include additional sub-processes, such as:selecting a ranked sub-group of variables affecting degradation of a GTfrom a larger group of variables affecting degradation of the GT. Thatis, in various embodiments thirty (30) or more variables may affectdegradation of a GT. According to various embodiments, the top 5-10variables may be selected. It is understood that the GT has a mean longterm degradation (in MW output), as is known in the art. Additionally,the log-normal type distribution of that mean long-term degradation hasa minimum degradation (zero), and a defined maximum degradation, as isknown in the art. According to various embodiments, additional processescan include simulating degradation of the selected set of variables fora nominal (P50) GT, and calculating MW output. Additionally, the processcan include iteratively adjusting the degradation values for thoseselected variables until the MW output matches the mean long-termdegradation (in MW) at a defined number of operating hours (e.g., 12,000hours). This process can also be repeated for the maximum degradation atthe same defined number of operating hours. The resulting min-mean-maxvalues will allow for estimation of the parameters of log normal-typedistributions (e.g., mean and variance) for each of the selected set ofdegradation variables.

FIG. 9 shows an illustrative environment 802 demonstrating thecontroller (control system 18) coupled with the GTs 10 via at least onecomputing device 814. As described herein, the control system 18 caninclude any conventional control system components used in controlling agas turbine engine (GT). For example, the control system 18 can includeelectrical and/or electro-mechanical components for actuating one ormore components in the GT(s) 10. The control system 18 can includeconventional computerized sub-components such as a processor, memory,input/output, bus, etc. The control system 18 can be configured (e.g.,programmed) to perform functions based upon operating conditions from anexternal source (e.g., at least one computing device 814), and/or mayinclude pre-programmed (encoded) instructions based upon parameters ofthe GT(s) 10.

The system 8042 can also include at least one computing device 814connected (e.g., hard-wired and/or wirelessly) with the control system18 and GT(s) 10. In various embodiments, the computing device 814 isoperably connected with the GT(s) 10, e.g., via a plurality ofconventional sensors such as flow meters, temperature sensors, etc., asdescribed herein. The computing device 814 can be communicativelyconnected with the control system 18, e.g., via conventional hard-wiredand/or wireless means. The control system 18 is configured to monitorthe GT(s) 10 during operation according to various embodiments.

Further, computing device 814 is shown in communication with a user 836.A user 836 may be, for example, a programmer or operator. Interactionsbetween these components and computing device 814 are discussedelsewhere in this application.

As noted herein, one or more of the processes described herein can beperformed, e.g., by at least one computing device, such as computingdevice 814, as described herein. In other cases, one or more of theseprocesses can be performed according to a computer-implemented method.In still other embodiments, one or more of these processes can beperformed by executing computer program code (e.g., control system 18)on at least one computing device (e.g., computing device 814), causingthe at least one computing device to perform a process, e.g., tuning atleast one GT 10 according to approaches described herein.

In further detail, computing device 814 is shown including a processingcomponent 122 (e.g., one or more processors), a storage component 124(e.g., a storage hierarchy), an input/output (I/O) component 126 (e.g.,one or more I/O interfaces and/or devices), and a communications pathway128. In one embodiment, processing component 122 executes program code,such as control system 18, which is at least partially embodied instorage component 124. While executing program code, processingcomponent 122 can process data, which can result in reading and/orwriting the data to/from storage component 124 and/or I/O component 126for further processing. Pathway 128 provides a communications linkbetween each of the components in computing device 814. I/O component126 can comprise one or more human I/O devices or storage devices, whichenable user 836 to interact with computing device 814 and/or one or morecommunications devices to enable user 136 and/or CS 138 to communicatewith computing device 814 using any type of communications link. To thisextent, CC plant load monitoring system 16 can manage a set ofinterfaces (e.g., graphical user interface(s), application programinterface, and/or the like) that enable human and/or system interactionwith control system 18.

In any event, computing device 814 can comprise one or more generalpurpose computing articles of manufacture (e.g., computing devices)capable of executing program code installed thereon. As used herein, itis understood that “program code” means any collection of instructions,in any language, code or notation, that cause a computing device havingan information processing capability to perform a particular functioneither directly or after any combination of the following: (a)conversion to another language, code or notation; (b) reproduction in adifferent material form; and/or (c) decompression. To this extent, CCplant load monitoring system 16 can be embodied as any combination ofsystem software and/or application software. In any event, the technicaleffect of computing device 814 is to tune at least one GT 10 accordingto various embodiments herein.

Further, control system can be implemented using a set of modules 132.In this case, a module 132 can enable computing device 814 to perform aset of tasks used by control system 18, and can be separately developedand/or implemented apart from other portions of control system 18.Control system 18 may include modules 132 which comprise a specific usemachine/hardware and/or software. Regardless, it is understood that twoor more modules, and/or systems may share some/all of their respectivehardware and/or software. Further, it is understood that some of thefunctionality discussed herein may not be implemented or additionalfunctionality may be included as part of computing device 814.

When computing device 814 comprises multiple computing devices, eachcomputing device may have only a portion of control system 18 embodiedthereon (e.g., one or more modules 132). However, it is understood thatcomputing device 814 and control system 18 are only representative ofvarious possible equivalent computer systems that may perform a processdescribed herein. To this extent, in other embodiments, thefunctionality provided by computing device 814 and control system 18 canbe at least partially implemented by one or more computing devices thatinclude any combination of general and/or specific purpose hardware withor without program code. In each embodiment, the hardware and programcode, if included, can be created using standard engineering andprogramming techniques, respectively.

Regardless, when computing device 814 includes multiple computingdevices, the computing devices can communicate over any type ofcommunications link. Further, while performing a process describedherein, computing device 814 can communicate with one or more othercomputer systems using any type of communications link. In either case,the communications link can comprise any combination of various types ofwired and/or wireless links; comprise any combination of one or moretypes of networks; and/or utilize any combination of various types oftransmission techniques and protocols.

As discussed herein, control system 18 enables computing device 814 tocontrol and/or tune at least one GT 10. Control system 18 may includelogic for performing one or more actions described herein. In oneembodiment, control system 18 may include logic to perform theabove-stated functions. Structurally, the logic may take any of avariety of forms such as a field programmable gate array (FPGA), amicroprocessor, a digital signal processor, an application specificintegrated circuit (ASIC) or any other specific use machine structurecapable of carrying out the functions described herein. Logic may takeany of a variety of forms, such as software and/or hardware. However,for illustrative purposes, control system 18 and logic included thereinwill be described herein as a specific use machine. As will beunderstood from the description, while logic is illustrated as includingeach of the above-stated functions, not all of the functions arenecessary according to the teachings of the invention as recited in theappended claims.

In various embodiments, control system 18 may be configured to monitoroperating parameters of one or more GT(s) 10 as described herein.Additionally, control system 18 is configured to command the one or moreGT(s) 10 to modify those operating parameters in order to achieve thecontrol and/or tuning functions described herein.

It is understood that in the flow diagram shown and described herein,other processes may be performed while not being shown, and the order ofprocesses can be rearranged according to various embodiments.Additionally, intermediate processes may be performed between one ormore described processes. The flow of processes shown and describedherein is not to be construed as limiting of the various embodiments.

In any case, the technical effect of the various embodiments of theinvention, including, e.g., the control system 18, is to control and/ortune one or more GT(s) 10 as described herein.

In various embodiments, components described as being “coupled” to oneanother can be joined along one or more interfaces. In some embodiments,these interfaces can include junctions between distinct components, andin other cases, these interfaces can include a solidly and/or integrallyformed interconnection. That is, in some cases, components that are“coupled” to one another can be simultaneously formed to define a singlecontinuous member. However, in other embodiments, these coupledcomponents can be formed as separate members and be subsequently joinedthrough known processes (e.g., fastening, ultrasonic welding, bonding).

When an element or layer is referred to as being “on”, “engaged to”,“connected to” or “coupled to” another element or layer, it may bedirectly on, engaged, connected or coupled to the other element orlayer, or intervening elements or layers may be present. In contrast,when an element is referred to as being “directly on,” “directly engagedto”, “directly connected to” or “directly coupled to” another element orlayer, there may be no intervening elements or layers present. Otherwords used to describe the relationship between elements should beinterpreted in a like fashion (e.g., “between” versus “directlybetween,” “adjacent” versus “directly adjacent,” etc.). As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

We claim:
 1. A system comprising: at least one computing deviceconfigured to tune a set of gas turbines (GTs) by performing actionsincluding: commanding each GT in the set of GTs to a base load level,based upon a measured ambient condition for each GT; commanding each GTin the set of GTs to adjust a respective output to match a nominalmega-watt power output value, and subsequently measuring an actualemissions value for each GT; adjusting an operating condition of each GTin the set of GTs based upon a difference between the respectivemeasured actual emissions value and a nominal emissions value at theambient condition; and calculating a degradation for each GT in the setof GTs over a period.
 2. The system of claim 1, wherein the base load isassociated with a mega-watt power output value and an emissions valuefor the measured ambient condition.
 3. The system of claim 1, wherein inresponse to commanding each GT in the set of GTs to the base load level,each GT does not attain at least one of the nominal MW output value orthe nominal emissions value.
 4. The system of claim 1, wherein the atleast one computing device is further configured to convert thedifference between the respective measured actual emissions value andthe nominal emissions value for each GT into a difference between arespective mega-watt power output value and the nominal mega-watt poweroutput value at the ambient condition value for each GT.
 5. The systemof claim 4, wherein the adjusting of the operating condition of each GTincludes adjusting the operating condition of each GT in the set of GTsby a fixed fraction of the difference between the respective mega-wattpower output value and the nominal mega-watt power output value, suchthat the output of each GT approaches and then reaches a respectivenominal mega-watt power output value, wherein the adjusting of theoperating condition of each GT in the set of GTs by the fixed fractionof the difference between the respective mega-watt power output valueand the nominal mega-watt power output value aligns each GT on a line ingraphical space plotting mega-watts versus emissions that is orthogonalto a nominal mega-watt power output/nominal emissions characteristic forthe each GT.
 6. The system of claim 1, wherein the calculating of thedegradation of each GT includes: selecting a set of degradationvariables each with a randomly selected degradation value for the set ofGTs; commanding each GT in the set of GTs to the base load level basedupon the adjusted operating condition; and calculatingmega-watt-emissions-firing temperature parameters for each GT in the setof GTs after the commanding of each GT in the set of GTs to the baseload level.
 7. The system of claim 6, wherein the selecting of the setof degradation variables includes: selecting a ranked sub-group ofvariables affecting degradation of a type of the GT from a larger groupof variables affecting degradation of the GT type.
 8. A computer programproduct comprising program code, which when executed by at least onecomputing device, causes the at least one computing device to tune a setof gas turbines (GTs) by performing actions including: commanding eachGT in the set of GTs to a base load level, based upon a measured ambientcondition for each GT; commanding each GT in the set of GTs to adjust arespective output to match a nominal mega-watt power output value, andsubsequently measuring an actual emissions value for each GT; adjustingan operating condition of each GT in the set of GTs based upon adifference between the respective measured actual emissions value and anominal emissions value at the ambient condition; and calculating adegradation for each GT in the set of GTs over a period.
 9. The computerprogram product of claim 8, wherein the base load is associated with amega-watt power output value and an emissions value for the measuredambient condition.
 10. The computer program product of claim 8, whereinin response to commanding each GT in the set of GTs to the base loadlevel, each GT does not attain at least one of the nominal MW outputvalue or the nominal emissions value.
 11. The computer program productof claim 8, which when executed, causes the at least one computingdevice to convert the difference between the respective measured actualemissions value and the nominal emissions value for each GT into adifference between a respective mega-watt power output value and thenominal mega-watt power output value at the ambient condition value foreach GT.
 12. The computer program product of claim 11, wherein theadjusting of the operating condition of each GT includes adjusting theoperating condition of each GT in the set of GTs by a fixed fraction ofthe difference between the respective mega-watt power output value andthe nominal mega-watt power output value, such that the output of eachGT approaches and then reaches a respective nominal mega-watt poweroutput value, wherein the adjusting of the operating condition of eachGT in the set of GTs by the fixed fraction of the difference between therespective mega-watt power output value and the nominal mega-watt poweroutput value aligns each GT on a line in graphical space plottingmega-watts versus emissions that is orthogonal to a nominal mega-wattpower output/nominal emissions characteristic for the each GT.
 13. Thecomputer program product of claim 8, wherein the calculating of thedegradation of each GT includes: selecting a set of degradationvariables each with a randomly selected degradation value for the set ofGTs; commanding each GT in the set of GTs to the base load level basedupon the adjusted operating condition; and calculatingmega-watt-emissions-firing temperature parameters for each GT in the setof GTs after the commanding of each GT in the set of GTs to the baseload level.
 14. The computer program product of claim 13, wherein theselecting of the set of degradation variables includes: selecting aranked sub-group of variables affecting degradation of a type of the GTfrom a larger group of variables affecting degradation of the GT type.15. A computer-implemented method of tuning a set of gas turbines (GTs),performed using at least one computing device, the method comprising:commanding each GT in the set of GTs to a base load level, based upon ameasured ambient condition for each GT; commanding each GT in the set ofGTs to adjust a respective output to match a nominal mega-watt poweroutput value, and subsequently measuring an actual emissions value foreach GT; adjusting an operating condition of each GT in the set of GTsbased upon a difference between the respective measured actual emissionsvalue and a nominal emissions value at the ambient condition; andcalculating a degradation for each GT in the set of GTs over a period.16. The computer-implemented of claim 15, wherein the base load isassociated with a mega-watt power output value and an emissions valuefor the measured ambient condition, wherein in response to commandingeach GT in the set of GTs to the base load level, each GT does notattain at least one of the nominal MW output value or the nominalemissions value, the computer-implemented method further includingconverting the difference between the respective measured actualemissions value and the nominal emissions value for each GT into adifference between a respective mega-watt power output value and thenominal mega-watt power output value at the ambient condition value foreach GT.
 17. The method of claim 16, wherein the adjusting of theoperating condition of each GT includes adjusting the operatingcondition of each GT in the set of GTs by a fixed fraction of thedifference between the respective mega-watt power output value and thenominal mega-watt power output value, such that the output of each GTapproaches and then reaches a respective nominal mega-watt power outputvalue, wherein the adjusting of the operating condition of each GT inthe set of GTs by the fixed fraction of the difference between therespective mega-watt power output value and the nominal mega-watt poweroutput value aligns each GT on a line in graphical space plottingmega-watts versus emissions that is orthogonal to a nominal mega-wattpower output/nominal emissions characteristic for the each GT.
 18. Themethod of claim 15, wherein the commanding of each GT in the set of GTsto adjust a respective output to match the nominal MW output value movesan actual emissions value for each GT closer to the nominal emissionsvalue without matching the nominal emissions value.
 19. The method ofclaim 18, wherein the calculating of the degradation of each GTincludes: selecting a set of degradation variables each with a randomlyselected degradation value for the set of GTs; commanding each GT in theset of GTs to the base load level based upon the adjusted operatingcondition; and calculating mega-watt-emissions-firing temperatureparameters for each GT in the set of GTs after the commanding of each GTin the set of GTs to the base load level.
 20. The method of claim 19,wherein the selecting of the set of degradation variables includes:selecting a ranked sub-group of variables affecting degradation of atype of the GT from a larger group of variables affecting degradation ofthe GT type.